• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.563-564
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• 2018

최근 차량 네트워크에서 포그 컴퓨팅 기능을 적용하는 새로운 연구가 진행 중이다. 특히, 최근 개발 중인 차량 포그 컴퓨팅 기술은 교통 정보를 실시간으로 수집하여 빠른 처리가 가능하며 이를 장법을 통해 안정된 차량 및 교통 서비스를 제공할 수 있다. 이런 차량 포그 컴퓨팅 기술은 교통 정보 수집, 운전 정보 제공, 스마트 교통 통제 및 도로 안전 메시지 등의 정보를 차량에 빠르게 전달할 수 있다. 본 논문에서는 차량 포그 컴퓨팅 기술을 리뷰하고 교차로 환경에서 차량 포그 컴퓨팅 기반 안전 메시지 전달을 위한 교통 시스템을 제안한다. 특히, 제안하는 기법은 대기 시간에 민감한 안전 메시지를 빠르게 전송할 수 있는 장점을 가지고 있다.

• Smart Media Journal
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• v.8 no.1
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• pp.35-42
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• 2019

With IoT device technology developments, such devices now can perceive the surrounding environment and operate upon the condition, but a method for efficiently processing an enormous amount of IoT device data is required. The existing cloud computing has a transmission delay problem due to load and distance. Fog Computing, an environment to control IoT devices, therefore, emerged to solve this problem. In Fog Computing, IoT devices are located close to each other to solve the shortcomings of the cloud system. While many earlier studies on Fog Computing for IoT mainly focus on its structure and framework, we would like to propose an integrated Fog Computing platform that monitors, analyzes, and controls IoT devices.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.89-92
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• 2020

According to the various IoT(Internet of Things) services, there have been lots of task offloading researches for IoT devices. Since there are service response delay and core network load issues in conventional cloud computing based offloadings, fog computing based offloading has been focused whose location is close to the IoT devices. However, even in the fog computing architecture, the load can be concentrated on the for computing node when the number of requests increase. To solve this problem, the opportunistic fog computing concept which offloads task to available computing resources such as cars and drones is introduced. In previous fog and opportunistic fog node researches, the offloading is performed immediately whenever the service request occurs. This means that the service requests can be offloaded to the opportunistic fog nodes only while they are available. However, if the service response delay requirement is satisfied, there is no need to offload the request immediately. In addition, the load can be distributed by making the best use of the opportunistic fog nodes. Therefore, this paper proposes a delayed offloading scheme to satisfy the response delay requirements and offload the request to the opportunistic fog nodes as efficiently as possible.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.27-32
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• 2018

IoT technology is developing with various application areas in $4^{th}$ Industrial revolution. There are many users using the application services. Sensing data from various environment need to be transferred to cloud computing storage and store in the cloud storage. However, physical distance from the end node to cloud computing storage is far away, and it is not efficient to transfer data from sensors and store the sensing data in the cloud storage whenever sensing data happen. Therefore, Fog computing is proposed to solve these problems which can process and store the sensing data. However, Fog computing is new emerging technology, there is no standard security model and requirements. This research proposes to security requirements and security model for Fog computing to establish a secure and efficient cloud computing environment.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.250-255
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• 2020

For the resolution of the latency problem of the cloud center-based cloud computing, fog computing was proposed that allows end devices to offload computations to nearby fog nodes. In the fog computing, virtualized service images are placed on fog nodes and, if service images are placed close to end devices, the duplicate service image placement problem may occur. Therefore, in this paper, we propose a service image placement mechanism based on the logical fog network that reduces duplicate service images by considering the pattern of collected service requests. For the performance evaluation of the proposed mechanism, through simulations, we compare ours with the on-demand mechanism placing a service image upon the receipt of a service request. We consider the performance factors like the number of service images, the number of non-accommodated service requests, and the network cost.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.6
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• pp.60-71
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• 2019

As IoT systems such as smart farms and smart cities become popular, a large amount of data collected from many sensor nodes is sent to a server in the Internet, which causes network traffic explosion, delay in delivery, and increase of server's workload. To solve these problems, the concept of fog computing has been proposed to store data between IoT systems and servers. In this study, we implemented a software platform of the fog node and applied it to the prototype smart farm system in order to check whether the problems listed above can be solved when using the fog node. When the fog node is used, the time taken to control an IoT device is lower than the response time of the existing IoT device-server case. We confirmed that it can also solve the problem of the Internet traffic explosion and the workload increase in the server. We also showed that the intelligent control of IoT system is feasible by having the data visualization in the server and real time remote control, emergency notification in the fog node as well as data storage which is the basic capability of the fog node.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.333-335
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• 2017

모바일 사용과 사물 인터넷 기술의 발전으로 인해 이를 적용할 수 있는 분야가 점점 다양해지고 이러한 기술들을 이용하는 사용자들의 수도 비약적으로 증가하였다. 기존에 존재하던 클라우드 컴퓨팅 기술은 사물 인터넷 환경에 방대한 데이터를 실시간으로 처리하는데 적합하지 않으며, 이러한 문제를 해결하기 위해 사용자와 조금 더 근접한 영역에서 응답 시간을 최소화하고 병목 현상을 해결할 수 있는 포그 컴퓨팅이 제안되었다. 이 논문에선 클라우드 컴퓨팅의 혼잡도 증가와 속도 감소로 인한 문제점을 극복하기 위해 포그 컴퓨팅이라는 새로운 시스템의 개념과 이에 따른 보안 이슈를 정리하고, 연구방향을 제시한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.257-267
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• 2017

With the development of IoT (Internet of Things) technology, the application area has been diversified and the number of users using this service also has increased greatly. Real time big data generated by many IoT devices is no longer suitable for processing in a cloud computing environment. To solve this issue, fog computing is suggested which minimizes response time and makes real time processing suitable. However, security requirement for new paradigm called fog computing is not established until now. In this paper, we define models for fog computing, and the security requirements for the defined model.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.1224-1227
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• 2017

포그 컴퓨팅 기술은 물리적 환경과 빈번하게 상호작용이 일어나는 사이버-물리 시스템에서 네트워크의 엣지에 있는 시스템이 컴퓨팅 작업을 수행하도록 함으로써 지역의 데이터를 실시간으로 수집하고 처리할 수 있다. 본 논문에서는 스쿨존내에서 안전을 높이기 위한 횡단보도의 신호등에 포그 컴퓨팅 기술을 적용한다. 신호등 시스템은 횡단보도에 접근하는 자동차를 인지하고, 위험 상황을 미리 방지하기 위해 자동차를 제어할 수 있다. 실험을 위해 사물인터넷 기술을 이용해 소형 테스트베드를 만들었으며, 신호 정보를 변화시키며 실험을 수행한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.294-296
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• 2017

Cloud computing is a computing technique that uploads all the data from a cloud node to a cloud server and provides it to users as a service. This is difficult to provide services in real time depending on the network conditions. This is because it is necessary to download information to the remote site through the network, not the local area, and to download additional services to provide services in the cloud. So fog computing has been proposed as an alternative. In this paper, we propose an efficient data exchange technique between cloud, fog and user. The proposed fog provides services to users and collects and processes data. The cloud is responsible for the flow of data exchange and control between the fog. We propose a standard method for data exchange. The application for this is to process and service the information generated by the BAN (Body Area Network) in the fog, and the cloud serves as a mediator. This can resolve data heterogeneity between devices or services and provide efficient data movement.